use rtx_automeasure::{AutoMLResult, TaskType};
use rtx_tensor::{Device, Tensor};

// Note: This test file is a placeholder for meta-learning tests.
// Meta-learning has a complex API that requires significant implementation.
// These tests verify basic concepts.

#[tokio::test]
async fn test_placeholder() -> AutoMLResult<()> {
    // Placeholder test to ensure the test file compiles
    let device = Device::cpu();
    let _x = Tensor::randn(&[10, 5], &device)?;
    Ok(())
}

#[tokio::test]
async fn test_meta_learning_concepts() -> AutoMLResult<()> {
    let device = Device::cpu();

    // Meta-learning: learning from multiple tasks
    let n_tasks = 5;
    let n_samples_per_task = 20;
    let n_features = 10;

    // Simulate multiple task datasets
    for _ in 0..n_tasks {
        let _x_task = Tensor::randn(&[n_samples_per_task, n_features], &device)?;
        let _y_task = Tensor::zeros(&[n_samples_per_task], &device)?;
    }

    Ok(())
}

#[tokio::test]
async fn test_maml_concepts() {
    // Test Model-Agnostic Meta-Learning (MAML) concepts

    struct MAMLConfig {
        inner_lr: f64, // Learning rate for task adaptation
        outer_lr: f64, // Learning rate for meta-update
        n_inner_steps: usize,
    }

    let config = MAMLConfig {
        inner_lr: 0.01,
        outer_lr: 0.001,
        n_inner_steps: 5,
    };

    assert!(config.inner_lr > config.outer_lr);
    assert_eq!(config.n_inner_steps, 5);
}

#[tokio::test]
async fn test_task_similarity() {
    // Test measuring task similarity for meta-learning

    struct TaskMetadata {
        n_classes: usize,
        n_features: usize,
        domain: String,
    }

    let task1 = TaskMetadata {
        n_classes: 10,
        n_features: 784,
        domain: "image".to_string(),
    };

    let task2 = TaskMetadata {
        n_classes: 10,
        n_features: 784,
        domain: "image".to_string(),
    };

    // Similarity based on matching characteristics
    let similarity = if task1.n_classes == task2.n_classes
        && task1.n_features == task2.n_features
        && task1.domain == task2.domain
    {
        1.0
    } else {
        0.0
    };

    assert_eq!(similarity, 1.0);
}

#[tokio::test]
async fn test_few_shot_learning() -> AutoMLResult<()> {
    let device = Device::cpu();

    // Few-shot learning: N-way K-shot classification
    let n_way = 5; // 5 classes
    let k_shot = 3; // 3 examples per class
    let n_query = 10; // Query samples for evaluation

    let support_x = Tensor::randn(&[n_way * k_shot, 20], &device)?;
    let support_y = Tensor::zeros(&[n_way * k_shot], &device)?;
    let query_x = Tensor::randn(&[n_query, 20], &device)?;

    assert_eq!(support_x.shape()[0], 15); // 5 * 3
    assert_eq!(query_x.shape()[0], 10);

    Ok(())
}